[Numpy-discussion] Numpy array performance issue

Bruno Santos bacmsantos@gmail....
Thu Feb 25 06:04:20 CST 2010


After implementation all the possibilities we discuss yesterday mi fastest
version is this one:
index_nSize=numpy.arange(0,length,nSize)
lsPhasedValues = numpy.unique1d(aLoci[numpy.where(aLoci[index_nSize]>0)])
...

bigaLoci = (aLoci>=r)
k = (aLoci>=r).sum()


This is taking around 0.12s for my test cases.
The other version you proposed:

j = numpy.arange(length)
j_nSize_mask = ((j%nSize)==0)
lsPhasedValues = numpy.unique1d(aLoci[j_nSize_mask&aLoci>=0])

bigaLoci = (aLoci>=r)
q = (j_nSize_mask&bigaLoci).sum()
k = bigaLoci.sum()

This takes 0.75s for the same input.

With this I was able to speed up my code in a afternoon more than in the two
previous weeks. I don't have enough words to thank you.

All the best,
Bruno

2010/2/24 Robert Kern <robert.kern@gmail.com>

> On Wed, Feb 24, 2010 at 12:38, Bruno Santos <bacmsantos@gmail.com> wrote:
> > This is probably me just being stupid. But what is the reason for this
> peace
> > of code not to be working:
> > index_nSize=numpy.arange(0,length,nSize)
> > lsPhasedValues = set([aLoci[i] for i in xrange(length) if (i%nSize==0 and
> > aLoci[i]>0)])
> > lsPhasedValues1 = numpy.where(aLoci[index_nSize]>0)
>
> Because this is not correct. where() gives you indices where the
> argument is True; you want the values in aLoci. Chris misunderstood
> your request.
>
> --
> Robert Kern
>
> "I have come to believe that the whole world is an enigma, a harmless
> enigma that is made terrible by our own mad attempt to interpret it as
> though it had an underlying truth."
>  -- Umberto Eco
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